Sensor-based human activity recognition (HAR) is having a significant impact in wide range of applications smart city, home, and personal healthcare. Such deployment HAR systems often faces the annotation-scarcity challenge; that is, most techniques, especially deep learning require large number training data while annotating sensor very time- effort-consuming. Unsupervised domain adaptation ha...

Data-driven fault diagnosis methods often require abundant labeled examples for each type. On the contrary, real-world data is unlabeled and consists of mostly healthy observations only few samples faulty conditions. The lack labels imposes a significant challenge existing data-driven methods. In this paper, we aim to overcome limitation by integrating expert knowledge with domain adaptation in...

Unsupervised Domain Adaptation (UDA) aims to learn a classifier for the unlabeled target domain by leveraging knowledge from labeled source with different but related distribution. Many existing approaches typically domain-invariant representation space directly matching marginal distributions of two domains. However, they ignore exploring underlying discriminative features data and align cross...

Abstract Unsupervised domain adaptation (UDA) aims at enhancing the generalizability of classification model learned from labeled source to an unlabeled target domain. An established approach UDA is constrain classifier on intermediate representation that distributionally invariant across domains. However, recent theoretical and empirical research has revealed relying only invariance fails guar...

Pedestrian detection is a common task in the research area of video analysis and its results lay foundations wide range applications. It commonly known that under challenging illumination weather conditions, conventional visible cameras perform poorly this limitation could be catered using thermal imagery. But, due to fact annotated datasets are less available compared ones, paper we emphasis n...

In few-shot unsupervised domain adaptation (FS-UDA), most existing methods followed the learning (FSL) to leverage low-level local features (learned from conventional convolutional models, e.g., ResNet) for classification. However, goal of FS-UDA and FSL are relevant yet distinct, since aims classify samples in target rather than source domain. We found that insufficient FS-UDA, which could int...

Abstract The use of deep learning makes it possible to achieve extraordinary results in all kinds tasks related computer vision. However, this performance is strongly the availability training data and its relationship with distribution eventual application scenario. This question vital importance areas such as robotics, where targeted environment are barely available advance. In context, domai...

Enormously hard work of label obtaining leads to the lack enough annotated samples in hyperspectral imagery (HSI). The mentioned reality inferred unsupervised classification performance barely satisfactorily. Unsupervised domain adaptation is exploited for knowledge delivery from a labeled source boost on an unlabeled target domain. In this paper, we propose architecture with dense-based compac...